Clinical and Laboratory Characteristics of COVID-19 Patients with Fecal SARS-CoV-2 Detection at a Tertiary Hospital in Indonesia

Transmission prevention is important to prevent the spread of COVID-19. Although most cases are transmitted through droplets and aerosols, several studies have shown the possibility of transmission through fecal material. It is important to identify which patients are more likely to shed SARS-CoV-2 to raise awareness of the virus transmission via their feces. This study aims to determine the association of clinical and laboratory characteristics of COVID-19 patients with the SARS-CoV-2 detection in feces. From May to December 2020, fecal specimens from confirmed COVID-19 patients were collected, processed, and tested for the SARS-CoV-2 RNA presence. Clinical and laboratory parameters were compared between patients with and without SARS-CoV-2 RNA in their feces. Categorical variables were analyzed using Chi-square or Fisher's Exact test, whereas non-categorical variables were analyzed using Independent T test and Mann-Whitney U test. From 51 COVID-19 patients of whom fecal specimen were collected, SARS-CoV-2 RNA was found in the feces of 26 (50.9%). The SARS-CoV-2 presence in the feces was associated with cough (p=0.002), dyspnea (p=0.017), bilateral pneumonia (p=0.011), lower SARS-CoV-2 CT-values in nasopharyngeal and oropharyngeal swabs (p=0.015), and clinical severity (p=0.0023). In conclusion, several clinical characteristics contributing to the COVID-19 severity and higher SARS-CoV-2 viral load in the respiratory tract were associated with the SARS-CoV-2 RNA detection in feces. © 2023 by SPC (Sami Publishing Company).

Improving the Accuracy of Text Classification Using the over Sampling Technique in the Case of Sinovac Vaccine

The WHO has declared COVID-19 (Coronavirus Disease 2019) a global health emergency. Up to 19 November 2021, the total positive cases in Indonesia reached 4,252,705, of which 4,100,837 recovered, and 143,714 died. Therefore, vaccines have been developed to minimize COVID-19 transmission. There are some kinds of vaccines developed by several companies such as Sinovac, AstraZeneca, Pfizer, and Moderna. The general public has a different opinion on Sinovac vaccine on Twitter, where some people promote it while others reject it. Data used in this study were 1000 tweets about the Sinovac vaccine. During the dataset collection unequal distribution often occurs, where the number of labels is more on one side. Such a situation is called imbalance class. Imbalance class in a dataset can reduce classification performance. To overcome the imbalance class, this study used the Synthetic Minority Over-sampling Technique (SMOTE). The classification methods used were K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest, and TF-IDF was used to determine the weight of the words. The average rise of the accuracy value of the three algorithms after SMOTE optimization was 14%. The results of sentiment analysis for the Sinovac vaccine revealed a positive sentiment of 81%. Thus, it can be concluded that the Sinovac vaccine received a positive response from the public. © 2022 Institute of Advanced Engineering and Science (IAES).

Covid-19 Pandemic: Project-Based Learning as Interprofessional Learning Model to Improve Student With Special Needs’ Self Efficacy

There is a particular emphasis on education for special needs children. They have a right to an education that is appropriate to their needs. This study aimed to improve the self-efficacy of students with special needs who studied in inclusive elementary schools by using project-based learning. The research method used explorative qualitative with the interview, observation, and documentation instruments through triangulation. All instruments were analyzed in-depth, descriptive narrative. This research was carried out in the inclusive elementary schools, among others were five Public Elementary Schools (PES). The findings show that the learning approach of project-based learning at inclusive elementary schools in the covid-19 pandemic effectively improved the students with special need to be active, participatory and feel motivated to solve learning problems experienced by learning products produced. © 2022 Universidad de Valladolid. All rights reserved.

Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE

At the end of 2019, the world was hit by the COVID-19 virus, which caused a pandemic. Indonesia has become one of the countries that are affected by this pandemic. To control the COVID-19 pandemic, the government has made various efforts, one of which is the use of the PeduliLindunig app. To access the PeduliLindungi app, the public can download it from Google Play. Google Play enables its users to write reviews on the apps that have been downloaded. This study aims to determine the sentiment analysis on the PeduliLindungi application on Google Play using the Random Forest Algorithm with SMOTE. Based on this study, public sentiment towards the PeduliLindungi app on Google Play tends to be negative. The Random Forest and SMOTE algorithms are used to classify sentiment in this study. The implementation of Random Forest and SMOTE resulted in 71% accuracy, 70% recall, and 70% precision. © 2022 IEEE.

Determining factors of COVID-19 vaccination uptake among elderly in Indonesia

The elderly is the most vulnerable population group during coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, the vaccination uptake against COVID-19 among the seniors was considered low. This research aimed to point out the most significant factor as the recommendation for government to develop strategy in increasing COVID-19 participation among elderly. The variables are the health belief model, trust in health authorities and media, the experience regarding COVID-19, the general vaccination behaviour, and the novel contribution is the addition of the health protocol compliance as the determining factors of COVID-19 vaccination uptake action among the elderly in Indonesia. The online survey using a structured questionnaire obtained 213 respondents aged ≥55 years old. Structured equation modelling was employed to test the model. The result showed that the health belief model (β=0.296), trust in media and authorities (β=0.524), and general vaccination behaviour (β=0.319) significantly affect health protocol compliance. The health belief model (β=0.699), trust in media authorities (β=0.933), and health protocol compliance (β=0.406) significantly affected the COVID-19 vaccination uptake behaviour. This result gives the implication that the government should focus on improving the trust in media and authorities among the elderly. This action would improve the knowledge of COVID-19 and increase the vaccination coverage among the elderly. © 2022, Intelektual Pustaka Media Utama. All rights reserved.

Knowledge based orchestration: Response to the dynamic environmental COVID-19 pandemic

Orchestration is a form of organizational response to the challenges of erratic environmental change, through organizational knowledge-based and collective efforts. Collaborative efforts of organizations to achieve better performance through ordinary capability and dynamic capability. Orchestration is performed through a combination of operating capabilities and dynamic capabilities, which are known as dynamic bundles. Studies to find new gaps are needed to expand dynamic bundles. Resources are an important factor, so orchestration is important regarding capabilities and resources. The combination of knowledge with capabilities is a source of organizational success. Knowledge-based resources, which are specialized knowledge, increase environmental awareness capabilities and are an important factor for performance. This paper aims to explore the role of bundles or resource configurations and capabilities and their contribution to performance outcomes. This paper proposes a dynamic bundles orchestration that includes operational capabilities and dynamic capabilities as well as resources. In this research, the proposed resources are knowledge resources. Knowledge-based approach is important as a determinant of performance. This paper also contributes to conceptual knowledge in orchestration, or it can be called knowledge-based orchestration. Knowledge-based orchestration is expected to encourage organizations that have an impact on accelerating the recovery of the COVID-19 pandemic conditions. © IEOM Society International.

CLINICAL FEATURES OF DEATH CASES WITH COVID-19

Pantiwilasa dr. Cipto Hospital, Semarang, Indonesia, was a private tertiary hospital ap-pointed by the Public Health Office Semarang City to provide health services for suspected and confirmed COVID-19 patients. It is challenging due to the variety of clinical manifestations that occurred and the lack of existing research on this disease. The aim of this study is to examine the clinical characteristics of COVID-19 patients who have died. This was a cross sectional study. The study was conducted from May to July 2020. Obtained data included anamneses, physical examination, and adjunct examination. Altogether 113 patients were observed at the initial examination. Mean age was 53 years (standard deviation: 14.04 years), mean onset was 5.93 days (SD: 4.27 days). A total of 41 pa-tients non-survived (36.28%) with mean age 58.73 years (SD: 11.87 years). Mean respiratory rate and SpO(2) were 28.38 x/minute (SD: 1.71 x/minute) and 90.95% (SD: 8.84%). We found some variables that related to death were age older than 58 years old (p=0.009), had >= 2 comorbidi-ties (p=0.000), fever t>38 degrees C (p=0.001), respiration rate >28x/minute (p=0.010), SpO(2) <90% (p=0.000), blood lymphocyte count <1.2x10(3)/mu L (p= 0.014), neutrophil-to-lymphocyte ratio >7.4 (p=0.004), bronchopneumonia with other complications (p=0.011). Factors related to the death of COVID-19 patients were age older than 58 years old, had >= 2 comorbidities, body temperature above 38 degrees C, respiratory rate >28 x/minutes, oxygen saturation below 90%, neutrophil-to-lymphocyte ratio >7.4, lymphocyte count <1.2 x 10(3)/mu l, and x-ray result showing bronchopneumonia with other complications.

The emerging of dynamic capabilities for socially responsible operations and innovation development in the covid-19 pandemic situation

Attention to sustainability is important, along with the increasing pressure in organizations for sustainability programs. On its development, a number of researchers in the academic community argue on the understanding why and how corporations integrate the Triple Bottom Line (TBL) which includes social, environmental and economic performance. The COVID-19 pandemic situation encourages companies to play a role in sustainable development. Socially responsible is a part of social responsibility activities referring to the business responsiveness to society. Socially responsible operations is a form of Corporate Social Responsibility (CSR) activities. On the other hand, the researchers argue that business organizations are the source of important innovation, has social responsibility to direct public issues related to sustainability, where responsible innovation has a contribution to sustainable development. The aim of this study is to fill the literature gap by building a conceptual framework of socially responsible operation and socially responsible innovation during the COVID-19 pandemic. Another thing is also proposed the role of dynamic capabilities for sustainability. Dynamic capabilities are attractive because of their potential to improve the performance of organizational outcomes. Dynamic capabilities in this case include dynamic managerial capability and dynamic innovation capability in the efforts to develop company sustainability. © IEOM Society International.

Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images

COVID-19 pandemic caused a vast impact worldwide the imbalance between the number of tools for COVID-19 detection and the demand for COVID-19 tests from citizens has overwhelmed the government. To overcome this problem, artificial intelligence is utilized, specifically in the deep learning field. In this paper, we propose FJCovNet, a new deep learning model based on DenseNet121. FJCovNet managed to get an accuracy of 98.14%, surpassing Xception with an accuracy of 84,24%, VGG19 with an accuracy of 95.25%, and ResNet50 with accuracy of 91.53%. FJCovNet also managed to get less training time with 612 seconds, lesser than VGG19 with 808 seconds and ResNet50 with 809 seconds, and only slightly more than Xception with 609 seconds. © 2021 IEEE.

Molecular docking study for covid-19 of curcumin and tetrahydrocurcumin analog

Analogs of curcumin and tetrahydrocurcumin are compounds that have been studied for a long time for their biological activities such as anticancer, antibacterial, antioxidant, antidiabetes and antiviral. This research is aimed to study the potency of curucmin and tetrahydrocurcumin analogs as antiviral agent against papain-like protease/deubiquitinase protein in Covid-19 drug virtually by using molecular docking of the analogs against 3E9S protein with PLANTS and MOE used as the docking programs. The results show that THHGV-6 is the most promising compound by using MOE method while THA-104 is the most potent one by using plants method. THA-102 is the most potent compound among all the tested ligands by using both MOE and PLANTS method. None of curcumin analogs show antiviral potency but many analogs of tetrahydrocurcumin show antiviral potency against 3E9S. This is in which may be developed further as antiSARS-Covid.

A Modified Deep Convolutional Network for COVID-19 detection based on chest X-ray images

COVID-19 pandemic caused vast impact worldwide. Many efforts have been made to tackle the pandemic, including in the deep learning community. In this research, a modification of deep neural network based on Xception model is proposed. The model is used for COVID-19 detection based on the chest X-ray images. The proposed model implements two stacks of two dense layers and batch normalization. The layers addition is used to avoid overfitting of the proposed model. The performance of the proposed model is compared to Resnet50, InceptionV3 and Xception. The experiment result shows that the proposed model has better performance than the other models used in the research. However, its computational time is higher than the other models used in the research. © 2020 IEEE.